Feature (or corner) detection is a technique that is used in computer vision algorithms to extract useful structural information from images that can be utilized by higher level vision processing algorithms such as object detection or optical flow. Depending on the scene, some corner detectors, such as Harris corners, can produce a relatively large list of corners, many of which may be clustered together spatially. The larger the feature list, the more downstream processing may be required to process the list. Distance-based non-maximum suppression can be used to reduce the size of the list by retaining only the strongest features within a programmable pixel neighborhood.
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